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1.
传统的移动平均线方法在指示股票最佳交易时刻时具有无法避免的时间滞后性。该文建立了股市技术分析中的移动平均与小波变换的数学模型,然后采用db5小波用股市数据进行仿真,所得结果明显优于移动平均线方法,显示了小波变换在股市技术分析中的应用价值。  相似文献   

2.
郑士芹  王秀峰 《计算机工程》2007,33(10):199-201
提出了利用改进的克隆选择算法发现模糊规则的方法。在该方法中,对规则的评价函数不仅包含规则本身的置信度和蕴涵隶属度等特性,也包含表明规则对规则集整体性能影响程度的量化特性,即一致性贡献和完备性贡献。将该方法用于发现股票20日移动平均线与历史量价之间的模糊规则的仿真试验收到了满意结果。  相似文献   

3.
基于模糊控制方法的股票投资策略研究   总被引:1,自引:1,他引:1  
为了取得较好的股票投资效果,将模糊控制方法与经典股票理论中移动平均线法相结合,提出了一种股票短线投资策略,由计算机代替人进行判断和选择,可以克服人的感情因素影响,实现更加理性的投资决策。计算机仿真实验表明,该方案是可行的。  相似文献   

4.
《软件》2020,(1):178-182
为了提高股票预测的正确率,参照股票研究的指标体系,以股票的相对强弱、变动速率、能量潮、异同移动平均线以及威廉指标五个纯技术指标作为股票预测的特征。通过网格搜索对随机森林的参数进行了优化,构建基于纯技术指标的和参数优化随机森林的股票预测模型,并以平安银行、万科、深振业A、神州高铁、美丽生态2017年4月30日到2019年6月30日所有交易日作为实验室数据,实验结果与原始随机森林、决策树以及支持向量机分类模型对比,证实了参数优化后的随机森林股票预测模型在模型评价中的准确率和AUC值都高于其他模型。  相似文献   

5.
将遗传程序设计应用到股票价格分析,在股票市场各种因素相互作用与影响很难厘清的情况下,只从个别因素(价格)入手,测试对单一因素预测所能达到的效果;提出了两种预测方法:对不同尺度的股票移动平均线进行预测和对股票价格数据进行平滑预处理之后所进行的中长期预测。通过遗传程序设计算法,寻找前几个时间单位的股票价格对本期股票价格影响的经验公式,以期反映价格变动的规律。计算机实验模拟表明,该方法对于平均线的预测和中长期预测有较好的效果。  相似文献   

6.
丁圣  高风 《计算机仿真》2006,23(11):259-262
股票市场是一个复杂的非线性动态系统,利用传统的时间序列预测技术很难揭示其内在规律,而近十几年来发展起来的神经网络理论逐渐成为非线性动态系统预测与建模的强有力工其。该文介绍了小波分析中的趋势提取技术,建立小波分析与神经网络相结合的预测模型,将该模型应用于股票平均线交易规则中,同时还与普通神经网络预测模型进行厂对比,研究实例表明,小波神经网络方法提高了预测精度,对移动平均线交易规则作了一种有效的补允,是股市技术分析的一种自效实用的方法。  相似文献   

7.
林恒建 《福建电脑》2014,(11):167-167
在开发证券交易系统时会遇到图形程序的编写,本文阐述K线、指数移动平均线的定义、计算方法及计算机实现,指数移动平均线的交易法则。  相似文献   

8.
移动互联网可以提供方便、真实的个性化信息,采用JSP技术结合WML能够开发动态WAP网站。本文详细介绍了WAP技术及采用JSP技术实现移动互联网上股票业务的构建过程和代码。  相似文献   

9.
股票价格预测总是投资者和技术分析者感兴趣的一个主题.然而,决定买卖股票的最好时间仍然是困难的,因为有很多因素可能影响股票价格.通过改进模糊决策树建立了一个新型金融时间序列数据预测模型.该预测模型融合数据聚类技术,模糊决策树及遗传算法来构建基于历史数据和技术指标的一个决策系统.提出的GAFDT模型在与各种股票的其它方法相比较时有平均预测准确率为0.82的最好绩效.  相似文献   

10.
国际投资     
国际证券投资国际证券投资是通过在国际间发行或买卖股票和债券的方式所进行的一种投资活动。证券一般分为债券和股票两种。持有某企业股票,就成为该企业的股东,就可以凭票定期分取股息和红利,也可出卖股票;持有某公司或政府的债券,就成为该公司或政府的债权人,可以凭券领取利息,到期收回本金。所以购买证券就是一种投资方式,相对应的发行证券则是一种集资方式。国际债券的发行方式分为公募与私募两种。公募是指承购公司将接受的新发行的债券向特定的投资者售出债券,采用公开制度。私募是债券发行者经过承购公司只向有限的投资者销…  相似文献   

11.
In this paper, we present a novel methodology for stock investment using the technique of high utility episode mining and genetic algorithms. Our objective is to devise a profitable episode-based investment model to reveal hidden events that are associated with high utility in the stock market. The time series data of stock price and the derived technical indicators, including moving average, moving average convergence and divergence, random index and bias index, are used for the construction of episode events. We then employ the genetic algorithm for the simultaneous optimization on parameters and selection of subsets of models. The empirical results show that our proposed method significantly outperforms the state-of-the-art methods in terms of annualized returns of investment and precision. We also provide a set of Z-tests to statistically validate the effectiveness of our proposed method. Based upon the promising results obtained, we expect this novel methodology can advance the research in data mining for computational finance and provide an alternative to stock investment in practice.  相似文献   

12.
邬保明 《计算机科学》2008,35(7):288-291
模糊数学已经被大量地应用于工业控制,本文根据证券市场上投资者的思维过程与模糊逻辑的相似性,尝试把模糊学引入股票市场投机行为的控制,模拟股票市场上投资者的股票买卖行为,旨在探索一种基于模糊逻辑的自动股票投机和投资逻辑,可以为股票软件开发提供理论依据和逻辑模型,建立在模糊逻辑基础上的股票软件可以引导投资者避免追涨杀跌,从而有利于股市稳定而健康地发展.  相似文献   

13.
Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.  相似文献   

14.
股票市场不仅是上市公司的重要融资渠道,也是重要的投资市场,股票预测一直受到人们的关注。为了充分利用来自不同股票价格的信息,提高股票的预测效果,提出一种多尺度股票价格预测模型TL-EMD-LSTM-MA(TELM)。TELM模型通过经验模态分解将收盘价分解为多个时间尺度分量,不同时间尺度分量震荡频率不同,反映了不同的周期性信息;根据分量的震荡频率选择不同方法进行预测,高频分量利用深度迁移学习的方法训练堆叠LSTM,低频分量利用移动平均法进行预测;将所有分量的预测值相加作为收盘价的最终预测输出。通过深度迁移学习训练的堆叠LSTM,包含来自不同股票的信息,具备更多行业或市场的知识,能有效降低预测误差。利用移动平均法预测低频分量,更有效捕获股票的总体趋势。对中国A股市场内500支股票以及上证指数、深证成指等指数进行预测,结果表明,与其他模型相比,TELM预测误差最低,拟合优度最高。根据TELM预测的股票收盘价模拟股票交易过程,结果表明TELM投资风险低、收益高。  相似文献   

15.
An Intelligent Business Advisor System for Stock Investment   总被引:1,自引:0,他引:1  
This paper presents an intelligent system to assist small investors to determine stock trend signals for investment in stock business. A pilot system is built providing three main categories of technical and analysis theories, namely momentum, moving average, and support/resistance line. It has extensive graphic interface design to facilitate the usage of the system. For novice investors, the system is associated with tutoring features and it supports analysis study of the rationale behind some system recommendations. Skilful investors can explore the various theories for the prediction by means of adjusting the weightings, combinations and even some independent variables allocated by the intelligent system. General users can therefore formulate their investment strategies upon system recommendations under different investment criteria accordingly.  相似文献   

16.
This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.  相似文献   

17.
We propose a stock market portfolio recommender system based on association rule mining (ARM) that analyzes stock data and suggests a ranked basket of stocks. The objective of this recommender system is to support stock market traders, individual investors and fund managers in their decisions by suggesting investment in a group of equity stocks when strong evidence of possible profit from these transactions is available.Our system is different compared to existing systems because it finds the correlation between stocks and recommends a portfolio. Existing techniques recommend buying or selling a single stock and do not recommend a portfolio.We have used the support confidence framework for generating association rules. The use of traditional ARM is infeasible because the number of association rules is exponential and finding relevant rules from this set is difficult. Therefore ARM techniques have been augmented with domain specific techniques like formation of thematical sectors, use of cross-sector and intra-sector rules to overcome the disadvantages of traditional ARM.We have implemented novel methods like using fuzzy logic and the concept of time lags to generate datasets from actual data of stock prices.Thorough experimentation has been performed on a variety of datasets like the BSE-30 sensitive Index, the S&P CNX Nifty or NSE-50, S&P CNX-100 and DOW-30 Industrial Average. We have compared the returns of our recommender system with the returns obtained from the top-5 mutual funds in India. The results of our system have surpassed the results from the mutual funds for all the datasets.Our approach demonstrates the application of soft computing techniques like ARM and fuzzy classification in the design of recommender systems.  相似文献   

18.
The goal of this study is to construct an enhanced process based on the investment satisfied capability index (ISCI). The process is divided into two stages. The first stage is to apply the Process Capability Indices (PCI) for quality management so as to develop a new performance appreciation method. Investors can utilize the ISCI index to rapidly evaluate individual stock performance and then select those stocks which can lead to achieve investment satisfaction. In the second stage, a particle swarm optimization (PSO) algorithm with moving interval windows is applied to find the optimal investment allocation of the stocks in this portfolio. Based on those algorithms we can ensure investment risk control and obtain a more profitable stock investment portfolio.  相似文献   

19.
Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalizes the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.  相似文献   

20.
Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.  相似文献   

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